Automated detection of objects of interest in images has rapidly expanded in the last decade. For example, lesions caused by disorders of the eye, especially Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) can be detected through image analysis. One important motivation is the need for reproducible, efficient, early detection, or screening, of large at-risk populations. In particular, there is a preponderance of evidence that early detection and timely treatment of DR, the most common cause of blindness in the working age population of the United States and of the European Union, can prevent visual loss and blindness in patients with diabetes.
Most conventional efforts have focused on automatically detecting the lesions in color fundus photographs, as this is a non-invasive and cost-effective modality. One study of a large screening dataset has shown that, even though some automated detection algorithms achieve a detection performance comparable to that of human experts, further improvements are desirable before they translation into clinical practice. A primary step in automated detection of DR is the detection of microaneurysms, which are highly pathognomic and often the first sign of DR, although it has been shown that detecting other lesions can improve DR detection performance. However, current methods fail to sufficiently differentiate lesions from retinal blood vessels: when a more sensitive setting was used, false positives occurred on the vessels, while when a specific setting was used, lesions connected to or close to the vasculature were missed.
Several automated detection algorithms for detecting the lesions of AMD, the most common macular disease associated with aging and the most important cause of blindness and visual loss in the developed world, have also been published. In particular, these efforts have focused on drusen, the hallmark of AMD. One of the main challenges in detecting drusen is the ability to differentiate them from bright lesions caused by other diseases, in particular, DR's exudates and cotton wool spots, as well as subtle, drusen-like lesions such as the flecks related to Stargardt's disease. AMD tends to occur in older patients than Stargardt's disease, but both the individual drusen and flecks, as well as their distribution, are very similar.
Negative lesion confounders are typically structures that are similar looking, but not the target lesion such as vessel portions in the microaneurysm case and exudates, cotton-wool spots and Stargardt flecks in the drusen case. Positive lesion confounders are typically a subset of target lesions that are easily missed because they have specific properties that are substantially different from the standard case, but should still be detected. In the case of microaneurysm detection, these are microaneurysms connected to the vasculature, for example. In other words, negative lesion confounders are the false positives of simple detectors and positive lesion confounders are the false negatives of simple detectors. Typically, lesions are hardly detected if they are not specifically modeled. In both cases, lesions that are proximate to other lesions, including being fused with these other lesions, also are positive lesion confounders.
Additionally, retinal vessel bifurcations are important landmarks in retinal images. Their metrics are important for clinical evaluation as well as input for downstream image processing, such as retinal image registration and branching analysis. Because of their geometrical stability, they could be the feature points for image registration and change detection. They are important targets in the vessel segmentation problem and analysis of the retinal vessel trees. Accordingly, system and methods for detecting and/or differentiating objects of interest such as lesions, bifurcations, abnormalities, and/or other identifiable objects or collections of pixels, are desirable.